Abstract
Motion estimation approaches enable the robust prediction of successive camera poses when a camera undergoes erratic motion. It is especially difficult to make robust predictions under such conditions when using a constant-velocity model. However, motion estimation itself inevitably involves pose errors that result in the production of an inconsistent map. To solve this problem, we propose a novel 3D visual SLAM approach in which both motion estimation and stochastic filtering are performed; in the proposed method, visual odometry and Rao-blackwellized particle filtering are combined. First, to ensure that the process and the measurement noise are independent (they are actually dependent in the case of a single sensor), we simply divide observations (i.e., image features) into two categories, common features observed in the consecutive key-frame images and new features detected in the current key-frame image. In addition, we propose a key-frame SLAM to reduce error accumulation with a data-driven proposal distribution. We demonstrate the accuracy of the proposed method in terms of the consistency of the global map.
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Kim, J., Yoon, KJ., Kweon, I.S. (2011). Robust 3-D Visual SLAM in a Large-Scale Environment. In: Pradalier, C., Siegwart, R., Hirzinger, G. (eds) Robotics Research. Springer Tracts in Advanced Robotics, vol 70. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19457-3_31
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DOI: https://doi.org/10.1007/978-3-642-19457-3_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19456-6
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